Conversion timing prediction for networked advertising

ABSTRACT

A computer-implemented method for conversion timing inference. A conversion timing model is model is configured to predict a likelihood of conversion based on an entity&#39;s elapsed time since a qualified entry event. The conversion timing model is constructed based on a distribution of the conversion timespans of converters. Each conversion timespan describes a length of time between a qualified entry event and a conversion event for a converted entity. A notification of an opportunity to expose a candidate entity to networked content is received and the likelihood of conversion for the candidate entity is determined by: determining an elapsed time since a qualified entry event for the candidate entity and applying the conversion timing model to the elapsed time. A response to the notification based on the likelihood of conversion for the candidate entity is prepared. Timely responses may include the selection of customized content, customized advertising content or bid values.

BACKGROUND

Field of Invention

The invention relates to providing a timely and customized response tothe notification of an opportunity to expose a visitor to networkedcontent by predicting the conversion timing of the visitor.

Description of Related Art

Online advertisements can help drive a prospective customer through aconversion funnel. In many cases, advertisers will engage one or moreparties, such as media partners, demand side platform operators andintention marketing platform operators to help drive conversions withnetworked advertising exposures. A party may be monitored, paid orevaluated based on the number of conversions which are attributed totheir operations.

It is not unusual for a prospective customer to be exposed to multiplenetworked advertisements before they convert. Presumably, some of theseexposures contribute to driving conversions, while others don't. Manyattribution schemes are related to the timing of an advertisingexposure, such as a “last touch” attribution scheme, which attributescredit to the advertisement which most recently preceded the customer'sconversion. Assessing the likelihood of a prospective customer'sconversion is useful for parties working towards driving conversions.Additionally, it would be advantageous for a party which is compensatedbased on an attribution scheme which is related to the timing of anadvertising exposure, to estimate the conversion timing for aprospective customer so that they can take proper, timely action, suchas setting an appropriate bid price or formatting a creative which issuitable for the prospective customer's funnel stage.

Online vendors would also benefit from the ability to rapidly understandthe conversion timing of a prospective customer which is engaged ontheir website in order to provide custom content or customized offerswhich may be tuned to the prospective customer's likely funnel stage.

What is needed is a way to model conversion timing for a prospectivecustomer, so parties can take appropriate, timely action.

SUMMARY

Embodiments of the invention provide a system, method, andcomputer-readable medium for modeling conversion timing. In anembodiment, a conversion timing system constructs a conversion timingmodel based on a distribution of conversion timespans. Each conversiontimespan describes a length of time between a qualified entry event anda conversion event for a converted entity. A qualified entry event is anevent which indicates a prospective customer's entry into theadvertiser's funnel, such as a visit to an advertiser's web site or theexecution of a branded search. The conversion timing system receives anotification of an opportunity to expose a candidate entity to networkedcontent, such as a bid request from a real time bidding (RTB) exchangeor a notification that a prospective customer has requested content froma vendor's website. The conversion timing system determines an elapsedtime since a qualified entry event for the candidate entity. A responseto the notification is prepared by applying the conversion timing modelto the candidate entity's elapsed time, before the opportunity to exposethe candidate entity to networked content is consumed. For example, aresponse may comprise the selection of a bid price, an advertisingcreative, customized content or combinations thereof.

In an embodiment, the conversion timing system configures the conversionmodel to predict a likelihood of conversion within a time window basedon a prospective customer's elapsed time.

In an embodiment, the conversion timing system fits the distribution ofconversion timespans with a gamma mixture model. A gamma mixture modelmay be fitted with a pre-configured number of conversion modes or fittedwith a number of conversion modes received from an external resource. Insome cases, multiple gamma mixture models may be fitted using differentnumber of conversion modes, different weights per conversion mode orboth, and the model with the best fit may be selected for operation.Advantageously, the number of conversion modes and the weightings perconversion mode which provide the best fit may provide insight to theadvertiser by characterizing the number of different types of convertersand the relative populations sizes of the different types of converters.

In some embodiments, the conversion timing system can construct aconversion timing model parameterized by one or more features such as:features characterizing a converted entity. Advantageously, theresulting parameterized conversion timing model can be used to makepredictions related to candidate entities with previously unseencombinations of features.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example computing environment in accordance withan embodiment;

FIG. 2a illustrates an example of a distribution of conversion timespansfor a population of converters and an example of a model constructedfrom that distribution using a mixture of gamma distributions;

FIG. 2b illustrates an example of a distribution of conversion timespansfor a population of converters and four weighted gamma distributions,each representing a different conversion mode;

FIG. 3 is a high-level block diagram illustrating an example of acomputer for use as a conversion timing system, a content provider, anentity, a direct measurement system, an advertising exchange system, acampaign operations system and/or an event relay system of FIG. 1;

FIG. 4 is a flow chart illustrating an example of a method forconversion timing inference, according to an embodiment of theinvention;

FIG. 5a is a table illustrating seven funnel states for a funnel whichis modeled with three funnel events where the order of the funnel eventsin an entity's history is not considered;

FIG. 5B is a table illustrating fifteen funnel states for a funnel whichis modeled with three funnel events where the order of the funnel eventsin an entity's history is considered; and

FIG. 6 is a flow chart illustrating an example of a method forconversion timing inference, according to an embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a high-level block diagram of a computing environment forconversion timing prediction in accordance with an embodiment of theinvention. In particular, the conversion timing system 100 can be usedto estimate the likelihood of conversion for a candidate entity based onthe elapsed time since the candidate entity's entry into a conversionfunnel, as marked by a qualified entry event. A conversion funnel isdescribed with a conversion event and the important entry point (orpoints), such as a visit to an advertiser's home page, along with thelimitations which can be used to qualify valid entry events.

FIG. 1 illustrates an example computing environment in accordance withan embodiment. As shown in FIG. 1, the conversion timing system 100, acontent provider 110, an entity 120, a direct measurement system 130, anadvertising exchange system 140, a campaign operations system 150, andan event relay system 160. These elements are connected by acommunications network 170, such as a local area network, a wide areanetwork, a wireless network, an intranet, a cable network, a satellitenetwork, a cellular phone network, an optical network, the Internet orcombinations thereof.

Content provider 110 is a media channel which provides networked content111 over the communications network 170 to an entity 120. Examples ofcontent providers 110 can include web site operators, online vendors andcable television operators. In some cases, it can be useful to considera content provider as comprising multiple content providers. Forexample, a content provider such as a portal website operator canprovide multiple websites, such as localized versions of the samewebsite. Similarly, a clothing vendor's website may comprise multiplesub-sites or sections such as a women's clothing website, a men'sclothing website and a children's clothing website. Examples ofnetworked content 111 can include, but are not limited to, a webpage,advertising content, a portion of an online video clip, a portion of anetworked television program, a portion of a cable television program, aportion of a satellite television program, a portion of an InternetProtocol (IP) television program, a portion of an online audio clip, theresults of a keyword search from an online search engine, the results ofa request for directions from an online mapping service, interfaces formaking online purchases and interfaces for checking a credit cardaccount balance. In some examples, networked content 111 can be providedover the communications network 170, based on a request to contentprovider 110; in some examples, content provider 110 can push thenetworked content 111 over the communications network 170 to an entity120. Although only one content provider 110 is shown in FIG. 1 forclarity, any number of content providers may be connected to thecommunications network 170.

In various embodiments, entity 120 accesses networked content 111 fromcontent provider 110 over the communications network 170. Examples ofentities can include end-users, consumers, customers, softwareinstallations or hardware devices used to access content or combinationsthereof. Software installations can include a web browser instance,video viewer instance or set-top box software residing on a hardwaredevice. A hardware device can comprise a computer, personal digitalassistant (PDA), cell phone or set-top unit (STU) such as a STU used inconjunction with cable television service. A consumer is a person orgroup of people who access content. In some cases, an entity 120 cancomprise a combination of entities which are logically grouped togetherto represent individuals, households or groups of individuals who accessnetworked content 111 over a communications network 170. In some cases,an entity which receives networked content 111, such as a web page, froma content provider 110, such as a website, can also be called a visitorto content provider 110. Although only one entity 120 is shown in FIG. 1for clarity, any number of entities may be connected to thecommunications network 170.

As shown in the embodiment of FIG. 1, entity 120 is associated with anentity identifier 121. For example, a hardware device identifier such asa Media Access Control Address (MAC address) can be stored on a deviceassociated with the entity 120. A software identifier such as a cookievalue can be stored locally. In an embodiment, identifiers used toidentify entities can be wholly or partially composed remotely from anentity or a device associated with the entity. In an embodiment,identifiers used to identify entities can be wholly or partly storedremotely from an entity or a device associated with the entity. In somecases, an entity can have multiple identifiers, such as multiple firstparty cookies and multiple third party cookies, which can be used toidentify the entity to various systems.

A content provider 110 may be integrated with a measurement system, suchas direct measurement system 130. A direct measurement system 130collects information for an entity 120 in conjunction with an identifiersuch as entity identifier 121. When an entity 120 requests networkedcontent 111 from a content provider 110, such as a request from a webbrowser operating on a personal computer to display the content of a webpage of an online publisher, the content provider 110 can re-direct theentity 120 to submit a pixel request to the direct measurement system130. Based on the pixel request, and optional subsequent actions, thedirect measurement system 130 can collect information from the entity120 and information associated with the entity 120. Information iscollected in conjunction with an identifier, such as entity identifier121, in order to enable the maintenance of a coherent body ofinformation, such as event histories which document measurable eventssuch as the consumption of content, partial event histories, eventhistory scores, a portion of an event history consumption history, anevent history characterization, an event history digest, or combinationsthereof, for an entity over time.

Information can include a consumption history for an entity 120, such asrecords of the execution of measurable events, consumption events andattribute values. In some cases, the context of an event, such as theduration and quality of the consumption event, can be part of the eventhistory. In an example, information associated with the hardwareconfiguration, software configuration or volume settings during thedelivery of an audio advertisement can be collected and stored as partof the event history. In some cases, the event history associated withan entity 120 can describe interactions with the content, such aspausing an online video clip.

In some cases, the event history can document the viewability oraccessibility of content. For example, in some cases, content may bedelivered to an entity, such as a browser, but not viewed by theend-user operating the browser because the end-user quickly navigatedaway from the content before it finished loading. In other examples,viewability may be compromised because content may be obscured by anitem displayed on a monitor, such as a window; accessibility of theaudio portion of multimedia content may be compromised because thevolume was muted through hardware or software controls.

Attribute values can comprise known, collected, received,cross-referenced, inferred, estimated, processed and calculatedinformation related to an entity 120 which may or may not be part of anevent history. In some cases, attribute values can be derived from anevent history. The estimated household size associated with an entity120 and the average household income for the zip code associated with anentity 120 are examples of attribute values. In some cases, attributevalues can be assessed or described using probabilities. For example, anattribute value can reflect that there is a 90% chance that thehousehold income related to an entity earns between $75,000 and $100,000per year. In some cases, information can be received and processed intoa processed attribute value. For example, attribute values can compriseinformation which has been statistically obfuscated to address privacyconcerns.

In an embodiment, information collected by the direct measurement system130 is stored in a data repository 131, such as a database. In somecases, information collected by the direct measurement system 130 can beprocessed before it is stored in data repository 131. In someembodiments, histories or information related to entities may bereceived or collected by systems other than direct measurement systems.

An advertising exposure results from the delivery of advertising contentover a network 170 to an entity 120. Advertising exposures comprisecontent such as textual, graphical, video, audio or multi-media contentwhich may be embedded in or delivered in conjunction with web pages,networked games, multi-media presentations, video content, audio contentor combinations thereof. An advertising exposure can be a simpleexposure which does not require or enable end-user interaction. However,some examples of advertising exposures can enable end-user interaction,such as an online advertisement which allows an end-user toclick-through to the advertiser's web page; similarly, an advertisingexposure such as a paid search result can enable end-user interaction,such as a click-through to an advertiser's website.

An advertising exchange, such as advertising exchange system 140, is anelectronic marketplace for buying and selling advertising impressions,such as the opportunity to expose an entity to advertising contentdelivered over a network. In an example, a real time bidding (RTB)advertising exchange can support the rapid auction of advertisingimpressions as they become available. For example, the advertisingexchange system 140 can be notified by a content provider 110 after anentity 120 requests networked content 111 from the content provider 110and an opportunity to display an advertising impression to that entity120 becomes available. The advertising exchange system 140 can provide astream of bid opportunity records with each bid opportunity recorddescribing the opportunity to expose an entity to advertising contentwhich is available for bidding. For example, the advertising exchangemay provide information in a bid opportunity record which describes theadvertising impression, such as the size of the advertisement, theposition of the advertising impression with respect to a web page, thewebsite location of the advertising impression, the context or topic ofthe display webpage associated with the advertising impression. Theentity which is the intended recipient of the advertising impression maybe described in the bid opportunity record with information, such as thelocal time of day for that entity and an identifier. An advertisingexchange system 140 receives bids related to a bid opportunity, selectsthe winning bid and enables the winning bidder to send the advertisingcontent to the entity 120. In some cases, an advertising server system(not shown) may be integrated into the process, and the advertisingcontent may be served to the entity 120 by the advertising server systemon behalf of the winning bidder. For example, an advertising serversystem can comprise a web server for storing online advertising contentand delivering the online advertising content to entities, such aswebsite visitors, through a client device. A local advertising serversystem may only provide services for a single content provider, whereasthird-party advertising server systems can provide services inconjunction with multiple content providers.

A campaign operations system 150 operates an advertising campaign. Insome cases, a campaign may be operated on behalf of an advertiser by aparty, such as a media partner. A demand side platform (DSP) is anexample of a campaign operation system that can operate multipleadvertising campaigns on behalf of multiple advertisers. In an example,a campaign operations system 150 can be integrated with one or moreadvertising exchange systems 140, and receive a stream of bidopportunity records. The campaign operation system can assess a bidopportunity to determine the suitability of the bid opportunity for anadvertising campaign. In some cases, assessing the suitability of a bidopportunity can comprise assessing a bid opportunity with respect to anadvertising campaign to reflect a likelihood of conversion bycategorizing, scoring or rating the bid opportunity, the entityassociated with the bid opportunity or both. In some cases, a party mayprovide a campaign operation system with information which can be usedto rate, select or avoid entities, bid opportunities or both such as aranked or unranked list of entities which are good candidates forconversion for a campaign; similarly, a party may provide a campaignoperation system with a list of entities which should be avoided for acampaign (a black list).

In an embodiment, conversion timing system 100 comprises a modelingsystem 102, a notification manager 104 and a response system 106. Aconversion timing system 100 constructs a conversion timing model basedon a distribution of conversion timespans. The conversion timespansdescribe a length of time between a qualified entry event and aconversion event for a converted entity. In some cases, the conversiontiming system may receive a set of conversion timespans from an externalresource, such as, but not limited to, a direct measurement system. Insome cases, the conversion timing system may collect or receive a set ofhistories for converted entities and determine the conversion timespansby examining the histories in light of definitions of a qualified entryevent and a conversion event. In some cases, the conversion timingsystem may receive measurements corresponding to timestamps for entryevents, qualified entry events, conversion events or combinationsthereof from an external resource.

The conversion timing system configures the conversion timing model topredict a likelihood of conversion for an entity based on an elapsedtime since the entity's qualified entry event. An entry event indicatesthat an entity has entered an advertiser's funnel. A qualified entryevent meets one or more entry event criteria. Entry event criteria maybe received from an external resource such as an advertiser, a vendor orcontent provider. Entry event criteria can characterize the event venue.For example, entry event criteria can describe one or more valid networkaddresses, URLs (Uniform Resource Locators) channels, web pages ordomains which can qualify a media consumption event as an entry event,such as a list of partial or complete network addresses or URLs (UniformResource Locators) for the advertiser's home page, a product descriptionpage and the home web page for the advertiser's authorized re-seller. Anentry event criterion can establish quality requirements such as minimumviewability requirements, minimum residence times, requirements relatedto the device display resolution or combinations thereof. An entry eventcriterion can characterize the entities related to the entry event andmay qualify desirable entities, disqualify undesirable entities or both.For example, entry events may be qualified or disqualified based on therelated entity's geographic location, DMA or domain.

In some cases, an entity may be associated with more than one qualifiedentry event; in this case, a consistent method for determining theelapsed time can be established and applied consistently. For example,the elapsed time can be the time elapsed since the most recent qualifiedentry event or the time elapsed since the oldest qualified entry event.In another example, a method for determining the most importantqualified entry event may be established and the elapsed time can be thetime elapsed since the most important qualified entry event. Similarly,a converter may be associated with more than one qualified entry eventand a consistent method for determining the conversion timespan shouldbe established and applied consistently. For example, the conversiontimespan can describe the length of time between the newest qualifiedentry event and the conversion event or the length of time between theoldest qualified entry event and the conversion event. In anotherexample, a method for determining the most important qualified entryevent may be established and the conversion timespan can be the lengthof time between the most important qualified entry event and theconversion event.

An entry event may be the direct result of an end-user's interactionwith advertising content; for example, an end-user may click-throughadvertising content which directs their browser to an advertiser's homepage. In another example, an end-user can execute a search through asearch engine, and select a paid placement result or an organic searchresult presented by the search engine, resulting in a visit to the homepage of an advertiser.

Entry event criteria can be configured so that an entry event is notrequired to be the direct result of user interaction with content suchas advertising content, search results or paid search results. Forexample, an end-user may directly type a partial or complete URL stringinto a web page browser to arrive at an advertiser's home page; in thiscase, the entry event is unreferred. An unreferred entry event is notthe direct result of end-user interaction with content such asadvertising content, search results or paid search results. In someembodiments, more than one qualified entry event may be configured perfunnel.

In an example, an entry event may be characterized as the execution of abranded search. A branded search indicates interest in a specific brandby including keywords such as an advertiser's name or trademark; asearch using the keyword phrase “women's lace-up BrandX Shoes sale” isan example of a branded search because it includes the term “BrandX” andis not a relevant search. Another example of a branded search is anonline image search with input comprising an image including anadvertiser's product or logo.

A conversion event for a funnel is an event which characterizes thevendor's or advertiser's desired goal, such as an online purchase, thecompletion of an online registration form or the submission of an onlineform requesting a price quote. In some cases, an advertiser may beinterested in driving new prospective customers to their website; inthis case, the conversion event can be a prospective customer's firstvisit to the advertiser's website. Similarly, a content provider may beinterested in increasing their engagement with their audience and theconversion event can be an entity's fifth visit to the contentprovider's website this month.

Examples of networked conversion events include online transactions suchas online purchases. However, some embodiments of the invention supportconversion events involving off-network activities which are reportedover a network to the conversion timing system, such as a purchase at abrick-and-mortar store using a printed coupon downloaded from anetworked location or a cell phone coupon associable with the customer.In an example, an integrated point-of-sale system can detect the use ofthe paper or electronic coupon to complete a purchase, and providesnotification of the conversion event over the network to the conversiontiming system; in some cases, a network or a system, such as an eventrelay system 160 can relay notification of a conversion event to anetworked system, such as, but not limited to, a conversion timingsystem 100, a content provider 110, a direct measurement system 130 anda campaign operations system 150.

Similarly, examples of networked entry events include receiving mediacontent over a network, such as visiting an advertiser's home page.However, some embodiments of the invention support entry eventscomprising events which do not require receiving media content over anetwork, but are still reported over a network to the conversion timingsystem. For example, some customer loyalty cards may require onlineactivation, providing an opportunity to anonymously link a customerloyalty card to an entity's media consumption history. For example, thecustomer loyalty card account number could be subjected to a one-wayhash during registration and the hash value could be stored inconjunction with the entity's media consumption history without thecustomer loyalty card account number, preserving the privacy of thecustomer. In this case, an entry event can comprise an activity such asswiping a customer loyalty card at a networked kiosk in abrick-and-mortar store to accumulate frequent visitor points through thestore's loyalty program. In some cases, a network or system, such as anevent relay system 160 can relay notification of an entry event via anetwork, such as network 170 or through a networked system, such as, butnot limited to, an conversion timing system 100, a content provider 110,a direct measurement system 130 and a campaign operations system 150.

In some embodiments, the conversion timing system 100 can receivehistories which document events comprising entry events, conversionevents or combinations thereof; the conversion timing system may examinethe histories to determine conversion timespans, elapsed times or both.In some cases, one or more systems which can be operated or maintainedby different business entities may provide histories to the conversiontiming system; for example, one or more direct measurement systems,advertising exchange systems, campaign operations systems orcombinations thereof may provide histories. In some cases, histories canbe provided to the conversion timing system from entities, parties,content providers or advertisers.

Modeling system 102 constructs a conversion timing model based on adistribution of conversion timespans for a population of converters. Aconversion timespan describes a length of time between a qualified entryevent and a conversion event for a converted entity. The conversiontiming model is configured to predict a likelihood of conversion for anentity based on an elapsed time since a qualified entry event executedby the entity.

In an example, a conversion timespan may be represented with the lengthof time between a qualified entry event and a conversion event for aconverted entity. FIG. 2a illustrates an example of a distribution ofconversion timespans for a population of converters and an example of amodel 220 constructed from that distribution. In this example, theconversion timespans have been sorted into time buckets which are onehour wide; each open circle 210 represents the count of conversiontimespans which fell within a one hour time bucket. The conversiontiming model 220 has been constructed using bucketed timespans. In othercases, the conversion timing model may be constructed using individualtimespan values.

A variety of modeling techniques may be used to construct a conversiontiming model. In an embodiment, the conversion timing system fits thedistribution of conversion timespans with a gamma mixture model, withthe conversion timespans treated like waiting times. A typical elapsedtime between a qualified entry event and a conversion event may bedifferent for different categories of converters. For example, impulsebuyers, loyal long-time customers and bargain shoppers may havedifferent purchasing patterns or conversion modes. In another example,converters may be categorized by patterns in the time or day, day orweek or seasonality of their purchases; for example, some converters maytypically make purchases during the weekend days, while others maytypically make purchases during weekday evenings. A gamma mixture modelmay be fitted with a pre-configured number of conversion modes or fittedwith a number of conversion modes received from an external resource; aconversion mode may map to a converter category. In some cases, multiplegamma mixture models may be fitted using different numbers of conversionmodes and the gamma mixture model with the best fit may be selected foroperation. Advantageously, the number of conversion modes which providesthe best fit may provide insight to the advertiser by characterizing thenumber of different types of converters.

Fitting a gamma mixture model may include weighting each of the gammamodels to achieve a good fit to the data. The weighting of a gamma modelrepresenting a conversion mode can be interpreted as the portion of theconversion mode represented in the population of converters. FIG. 2billustrates an example of the four weighted gamma distributions whichwere used fit to the data 210. In this example, gamma distribution 250models a distribution of converters which strongly represents converterswhich convert rapidly with a conversion timespan of one day or less, andit has been given a relative weight of 0.5, meaning that this gammadistribution represents a conversion mode which accounts for 50% of theconverters. Gamma distribution 260 models a distribution of converterswhich strongly represents converters which convert with a conversiontimespan on the order of seven days, and it has been given a relativeweight of 0.3, meaning that this gamma distribution represents aconversion mode which accounts for 30% of the converters. Gammadistribution 270 models a distribution of converters which stronglyrepresents converters which convert with a conversion timespan on theorder of fourteen days, and it has been given a relative weight of 0.1,meaning that this gamma distribution represents a conversion mode whichaccounts for 10% of the converters. Gamma distribution 280 models adistribution of converters which strongly represents converters whichconvert with a conversion timespan on the order of twenty-eight days,and it has been given a relative weight of 0.1, meaning that this gammadistribution represents a conversion mode which accounts for 10% of theconverters.

In an example, the conversion timing model can be configured to predicta likelihood of conversion at a point in time or within a time window.For example, a party may want to know what the likelihood of conversionis at the moment the notification is received, one hour from the momentthe notification is received or both. Referring to FIG. 2a , thelikelihood of conversion at a point in time may be estimated by locatinga point on curve 220.

In some cases, a party may want to know the likelihood of conversionwithin a time window such as the likelihood of conversion within thenext hour or next day; to address this case, the conversion timing modelcan be configured to predict a likelihood of conversion within a timewindow. Similarly, a party may want to know the likelihood of conversionfor a time window in the future, such as the likelihood of conversionbetween tomorrow and the day after tomorrow. Referring to FIG. 2a , thelikelihood of conversion for a time window may be estimated byintegrating the area under a portion of the curve 220.

In some cases, the likelihood of conversion can be represented as acategory such as low likelihood of conversion, moderate likelihood ofconversion or high likelihood of conversion, with respect to a point intime or within a time window. In some cases, the conversion timingsystem 100 may determine categories, with ranges of values forlikelihood of conversion. In some cases, the categories, with ranges ofvalues, may be provided to the conversion timing system 100; in somecases, the number of categories may be provided to the conversion timingsystem 100 and the conversion timing system 100 may determine suitableranges of values for likelihood of conversion for each category. Theresponse to the notification can be prepared by the conversion timingsystem 100 to include the likelihood of conversion category.

Conversion timing system 100 can be configured to use a conversiontiming model to predict trends related to the likelihood of conversion.For example, the likelihood of conversion at a moment in time or for atime window may be estimated using a conversion timing model aspreviously described. In addition, the trend for the likelihood ofconversion over a trending time period may also be estimated from aconversion timing model. Examples of trends include “rising over thenext day”, “falling between noon and 3:00 PM today” and “substantiallystable over the next week.” In some cases, trending information and thelikelihood of conversion for a candidate entity may be combined in acategory which describes both. For example, a category may indicate thatthe likelihood of conversion is moderate at a point in time, but risingover the next week; another category may indicate that the likelihood ofconversion is high during a time window, but falling between noon and3:00 PM that day.

Modeling system 102 constructs a conversion timing model based on adistribution of conversion timespans for a population of converters. Insome cases, additional information may be available to the modelingsystem 102, such as, but not limited to, information related to aconverter. For example, information related to the converter's mediaconsumption history, ad exposure history, purchasing history orcombinations thereof may be made available to the modeling system 102.In some cases, known or inferred geographic, sociographic, psychographicand/or demographic information may be made available to the modelingsystem 102. In some cases, additional information related to aconversion may be made available to the modeling system 102, such astypical trends for an industry related to time-of-day, day-of-week orseasonality of conversions. With access to additional information, themodeling system 102 may construct a parameterized conversion timingmodel.

In an example, additional information may be used to adjust theselection of the gamma distributions which are incorporated into thegamma mixture model, the relative weighting of the gamma distributionsused in the gamma mixture model or combinations thereof.

A notification manager 104 receives notification of an opportunity toexpose a candidate entity to networked content. For example, thenotification may comprise a bid opportunity received from an advertisingexchange system 140, such as a real time bidding exchange (RTB). Inanother example, the notification may comprise a message from a contentprovider 110 seeking information which can be used to inform contentcustomization, advertisement selection by the content provider 110 oradvertising content selection. The notification is associated with atimestamp. The notification's timestamp may be received by theconversion timing system 100 in conjunction with the notification; insome examples, the timestamp may be assigned by the conversion timingsystem 100. In some cases, the timestamp may be expressed using a knowntime zone, such as GMT, or expressed in elapsed time relative to a timeorigin, such as UNIX® time. In some cases, it may be necessary for theconversion timing system 100 to convert a timestamp to a common timestandard for consistency.

The notification manager 104 identifies a qualified entry event for thecandidate entity, according to one or more entry event criteria. Aspreviously discussed, the conversion timing model may be constructedusing elapsed times with respect to entry events which are qualifiedaccording to one or more entry event criteria; for accuracy, thenotification manager can operate using the same entry event criteria toassess the elapsed time for a candidate entity. However, in some cases,the notification manager and the modeling system 102 may use differentsets of entry event criteria. For example, a conversion timing model maybe constructed based on events with coarse time-stamps, and the modelingsystem may operate using an entry event criteria including a limitationexpressed in terms of a low resolution time-stamp. However, thenotification manager 104 may receive notifications which are associatedwith a precise time measurement, and an entry event criteria applied bythe notification manager 104 may be expressed in terms of a highresolution time-stamp. In another example, the entry event criteria usedfor construction of the conversion timing model may be specific to theconversions used to generate the model and the entry event criteria maybe updated or abstracted for use by the notification manager to assess acandidate entity's entry event.

A candidate entity's qualified entry event is associated with anapproximate or exact time that the qualified entry event occurred,enabling the determination of an elapsed time since a qualified entryevent. In some cases, a candidate's qualified entry event may beassociated with a timestamp expressed using a known time zone, such asGMT, or expressed in a length of time relative to a time origin, such asUNIX® time. In some cases, it can be necessary to convert a timestamp toa common time standard for consistency. In some cases, the responsesystem 106 can also access a partial or complete media consumptionhistory for a candidate entity to determine a timestamp for a quantifiedentry event. The elapsed time can be determined by assessing the timeelapsed between a timestamp associated with a notification related to acandidate entity and a timestamp associated with a qualified entry eventfor the candidate entity.

Response system 106 receives a conversion timing model from the modelingsystem 102 and an elapsed time for a candidate entity from thenotification manager, and predicts a likelihood of conversion for thecandidate entity by applying the conversion timing model to the elapsedtime. A response to the notification is prepared based on the likelihoodof conversion before the notification's opportunity to expose thecandidate entity to networked content is consumed. In some cases, theresponse can comprise the value of the likelihood of conversion at apoint in time, within a time window or both. In some cases, the responsesystem 106 may take steps to process the likelihood of conversion intoone or more useful parameters such as, but not limited to, a suggestedbid value, suggested custom content, suggested ad content, suggested adcategory and estimated funnel stage. The response may be provided tosystems such as, but not limited to, a content provider, an advertisingexchange system, an advertising content server, and a campaignoperations system.

In an example, the conversion timing system 100 can be integrated withsystems that provide information such as event histories, enabling it toreceive information and consistently associate the information with thecorrect entity. For example, a direct measurement system may beintegrated with the conversion timing system. For example, contentproviders can redirect an entity to submit a pixel request to a directmeasurement system as previously described. For example, the pixelrequest can be a request for a redirection script from the directmeasurement system. After receiving a pixel request from the entity, thedirect measurement system 130 may generate an anonymous identifier whichit stores in its data repository 131 in conjunction with the entityconsumption history, and also sends back to the entity as a variable inan HTML meta data field; in addition, the direct measurement system cansend a redirection script to the entity, which instructs the entity toprovide the anonymous identifier to the conversion timing system alongwith a pixel request to the conversion timing system. This enables theconversion timing system to set or reset a conversion timing systemthird party cookie at the entity. The conversion timing system can nowcross-reference their own cookie or entity identifier with the directmeasurement system's anonymous identifier so that the direct measurementsystem can provide information such as an event history associated withan anonymous identifier, making the information received from the directmeasurement system associable with the conversion timing system's entityidentifier. This describes just one possible method of integrating withexternal systems.

In an embodiment, a conversion timing system can comprise internalmodules that operate as a direct measurement system, a campaignoperations system and/or an advertising exchange system. This can enablethe use of the same entity identifier in all internal modules or theability to easily establish the cross-reference between the differentidentifiers used by the different internal modules.

FIG. 3 is a high-level block diagram illustrating an example of acomputer for use as a conversion timing system 100, a content provider110, an entity, a direct measurement system 130, an advertising exchangesystem 140, a campaign operations system 150 and/or an event relaysystem 150 of FIG. 1. Illustrated are a processor 302 coupled to a bus304. Also coupled to the bus 304 are a memory 306, a storage device 308,a keyboard 310, a graphics adapter 312, a pointing device 314, and anetwork adapter 316. A display 318 is coupled to the graphics adapter312.

The processor 302 may be any general-purpose processor. The storagedevice 308 is, in one embodiment, a hard disk drive but can also be anyother device capable of storing data, such as a writeable compact disk(CD) or DVD, or a solid-state memory device. The memory 306 may be, forexample, firmware, read-only memory (ROM), non-volatile random accessmemory (NVRAM), and/or RAM, and holds instructions and data used by theprocessor 302. The pointing device 314 may be a mouse, track ball, orother type of pointing device, and is used in combination with thekeyboard 310 to input data into the computer 300. The graphics adapter312 displays images and other information on the display 318. Thenetwork adapter 316 couples the computer 300 to the network (not shown).In one embodiment, the network is the Internet. The network can alsoutilize dedicated or private communications links that are notnecessarily part of the Internet.

As is known in the art, the computer 300 is adapted to execute computerprogram modules. As used herein, the term “module” refers to computerprogram logic and/or data for providing the specified functionality. Amodule can be implemented in hardware, firmware, and/or software. In oneembodiment, the modules are stored on the storage device 308, loadedinto the memory 306, and executed by the processor 302. The computer 300is configured to perform the specific functions and operations byvarious modules, for example as detailed in FIG. 4 and FIG. 6, andthereby operates as a particular computer under such program control.The types of computers 300 utilized by the entities of FIG. 1 can varydepending upon the embodiment and the processing power utilized by theentity.

FIG. 4 is a flow chart illustrating an example of a method forconversion timing inference according to an embodiment.

A conversion timing model is constructed at a conversion timing system100 based on a distribution of conversion timespans (Step 410). Aconversion timespan describes a length of time between a qualified entryevent and a conversion event for a converted entity and wherein theconversion timing model is configured to predict a likelihood ofconversion for an entity based on the entity's elapsed time since aqualified entry event. In some cases, the conversion timespans may besorted into buckets, with each bucket representing a range of timespans,such as timespans ranging between six and seven hours since a qualifiedentry event.

In some cases, the conversion timing model is constructed in Step 410,by fitting the distribution of conversion timespans with a gamma mixturemodel. The number of gamma distributions used in generating the gammamixture model may be received from an external resource. For example,the number of gamma distributions may correspond to the number ofconversion modes known or posited by an advertiser. In another example,the number of gamma distributions or conversion modes may be inferred bythe conversion timing system 100. In some cases, the conversion timingsystem 100 may generate multiple gamma mixture models, and the one withthe best fit may be selected.

A notification of an opportunity to expose a candidate entity tonetworked content is received (Step 420). In an example, thenotification may be a bid request from a real time bidding exchange(RTB) notifying the conversion timing system 100 that an opportunity toexpose a candidate entity to networked advertising content is availablefor purchase by the highest bidder. In some cases, an entity identifierfor the candidate entity, which is the intended recipient of thenetworked advertising content, may be included in the notification. Thecandidate entity's entity identifier can enable the conversion timingsystem 100 to access information related to the candidate entity, suchas the candidate entity's history.

In an example, a campaign operations system 150 may manage theinteraction with an exchange such as an RTB, and may receive a bidrequest from an RTB; the campaign operations system 150 may in turnrequest information from the conversion timing system, thereby notifyingthe conversion timing system 100 that an opportunity to expose acandidate entity to networked content is available.

An elapsed time since a qualified entry event for the candidate entityis identified (Step 430). In some cases, the conversion timing system100 may determine the elapsed time since a qualified entry event for acandidate entity. For example, the conversion timing system 100 mayaccess an event history for a candidate entity and determine the elapsedtime since a qualified entry event based on one or more receivedcriteria for qualifying an entry event.

A likelihood of conversion for the candidate entity is determined byapplying the conversion timing model to the elapsed time since aqualified entry event for the candidate entity (Step 440). In somecases, the likelihood of conversion may be determined for a point intime. In some cases, the likelihood of conversion may be determined fora time window.

A response to the notification is prepared based on the likelihood ofconversion for the candidate entity (Step 450). A response can comprisea bid value. In an example, the conversion timing system 100 may receivea bid request and manage the interaction with an advertising exchangesystem 140. For example, the conversion timing system 100 could requesta proposed bid value from a system such as a campaign operations system150, and then prepare a response by altering the proposed bid valuebased on the candidate entity's likelihood of conversion. In an example,the response can comprise the selection or suggestion of an advertisingcreative based on the candidate entity's likelihood of conversion. Inanother example, the conversion timing system 100 may receive a bidrequest and relay a prepared response to another system which isresponsible for managing the interaction with an advertising exchangesystem 140.

A response can comprise a bid value. For example, the conversion timingsystem 100 can receive a notification from a content provider, such asan online clothing vendor or an online news portal seeking to customizethe user experience for their visitors. The notification notifies theconversion timing system 100 of the availability of an opportunity toexpose a candidate entity to networked content. For example, an onlineclothing vendor may assume a funnel stage for a prospective customerbased on the likelihood of conversion based on an elapsed time, asestimated by the conversion timing system 100. When a prospectivecustomer requests content from the online clothing vendor, as they wouldwhen browsing the website of the online clothing vendor, the onlineclothing vendor may notify the conversion timing system 100 of theopportunity to expose a candidate entity to networked content byrequesting a measure or category of the likelihood of conversion basedon the elapsed time, and then choose to expose the prospective customerto customized content, an advertisement, a special offer or combinationsthereof which can be tuned to the presumed funnel stage of theprospective customer, based on the response from the conversion timingsystem 100. In an example, the notification is provided to theconversion timing system 100 after the opportunity to expose a candidateentity to networked content becomes available, and the response isprovided before the opportunity is consumed.

The conversion path to a conversion event for a prospective customer mayinclude multiple events. For example, before purchasing a product, acustomer may read an online article about the product, operate a searchengine with keywords that indicate the customer is seeking reviews ofthe product, operate a search engine with keywords that indicate thecustomer is seeking a vendor for the product, visit the website for themanufacturer of the product, visit the website for a vendor of theproduct, visit a specific page on the product vendor's website, orcombinations thereof. In an embodiment, the conversion paths of a set ofconverters may be analyzed by the conversion timing system 100 toproduce a conversion timing model for predicting a candidate entity'slikelihood of conversion based on the candidate entity's history. Thedescriptions for a set of two or more qualified funnel events areconfigured. A qualified funnel event is an event which indicates that aprospective customer is somewhere in the advertiser's funnel. In somecases, a qualified funnel event may be a qualified entry event. In somecases, a qualified entry event may comprise two or more qualified entryevents taken together.

FIG. 5a is a table illustrating seven funnel states for a funnel whichis modeled with three funnel events where the order of the funnel eventsin an entity's history is not considered. For FIG. 5a , “+” in the tableindicates that the event is found in the entity's consumption history; a“−” in the table indicates that the event is not found in the entity'sconsumption history. FIG. 5B is a table illustrating fifteen funnelstates for a funnel which is modeled with three funnel events where theorder of the funnel events in an entity's history is considered. ForFIG. 5b , a number (1, 2 or 3) indicates that the event can be found inthe entity's consumption history and the value of the number indicatesthe order in which the event occurred with respect to the other funnelevents; for example, a “1” indicates the oldest funnel event and a “3”indicates the most recent funnel event. A “−” in the table indicatesthat the event is not found in the entity's consumption history.

In an example, the conversion timing system 100 may produce a separateconversion timing model for each funnel state associated with a funnel.The conversion timing model for one funnel state would use the funnelstate description for each funnel state to establish the qualifyingcriteria for the funnel state's qualified entry event. For example, aqualified entry event for a conversion timing model for the funnel statedescribed by funnel state identifier ABC would require a convertedentity to have event A, event B and event C in their history; theconversion timespan could be measured from the time the last requiredevent occurred and the time of the conversion event. Similarly, aqualified entry event for a conversion timing model for funnel stageidentifier A→C→B for the funnel described n FIG. 5B would require aconverted entity to have event A, event C and event B in their history,in that relative order; the conversion timespan could be measured fromthe time the last required event (in this case, event B) occurred andthe time of the conversion event. However, it is understood that otherconsistent methods can be developed to consistently measure conversiontimespans.

For an example where the conversion timing system 100 produces aseparate conversion timing model for each funnel state, the conversiontiming system must select the proper conversion timing model to apply toa candidate entity in order to estimate a likelihood of conversion basedon the elapsed time. In this case, the funnel state of the candidateentity can be determined by examining the candidate entity's history,and the conversion timing model which corresponds to that funnel statecan be selected. For example, referring to the funnel described by FIG.5a , if a candidate entity's history includes event A, event B and eventC, then conversion timing model corresponding to funnel stage identifierABC may be selected and applied to the candidate entity's elapsed time.

FIG. 6 is a flow chart illustrating an example of a method forconversion timing inference according to an embodiment.

Converted entities are sorted into funnel states based on the definitionof an entry event for each funnel state (Step 610). The entry event fora funnel state comprises one or more qualified funnel events. In somecases, additional details such as the timing and/or sequence of theentry events may also be specified. For a converter, the funnel statedescribes the history of the converter prior to conversion. Like thepreviously described qualified entry events, there may be additionalrequirements.

A conversion timing model is constructed at a conversion timing system100 for each funnel state based on the distribution of conversiontimespans for the funnel states's converted entities (Step 620). Aconversion timespan describes a length of time between a qualified entryevent and a conversion event for a converted entity and wherein theconversion timing model is configured to predict a likelihood ofconversion for an entity based on the entity's elapsed time since aqualified entry event.

A notification of an opportunity to expose a candidate entity tonetworked content is received (Step 630). In an example, thenotification may be a bid request from a real time bidding exchange(RTB) notifying the conversion timing system 100 that an opportunity toexpose a candidate entity to networked advertising content is availablefor purchase by the highest bidder. In some cases, an entity identifierfor the candidate entity, which is the intended recipient of thenetworked advertising content, may be included in the notification. Thecandidate entity's entity identifier can enable the conversion timingsystem 100 to access information related to the candidate entity, suchas the candidate entity's history.

The candidate entity can be matched with a funnel state (Step 640). Forexample, the selection of a funnel state can be based on the presence ofqualified funnel events in the candidate entity's history. In somecases, a candidate entity may be matched with two or more funnel states.In some cases, only one of the funnel states will be selected for amatch. In an example, the most restrictive funnel state may be selected.For example, a candidate entity's history may match the funnel stagescorresponding to A, AB and ABC for the funnel described in FIG. 5A. Inthis case, the most restrictive funnel state is ABC and it may beselected for a match because it has the most requirements.

An elapsed time since a qualified entry event for the candidate entityis identified (Step 650). In some cases, the conversion timing system100 may determine the elapsed time since a qualified entry event for acandidate entity. For example, the conversion timing system 100 mayaccess an event history for a candidate entity and determine the elapsedtime since a qualified entry event based on one or more receivedcriteria for qualifying an entry event. As previously mentioned, when aqualified entry event comprises two or more entry events, there arevarious methods that can be used to identify the elapsed time. A methodshould be selected and applied consistently for the best results.

A likelihood of conversion for the candidate entity is determined byapplying the matching funnel state conversion timing model to theelapsed time (Step 660). In some cases, the likelihood of conversion maybe determined for a point in time. In some cases, the likelihood ofconversion may be determined for a time window.

A response to the notification is prepared based on the likelihood ofconversion for the candidate entity (Step 670).

In an example, the conversion timing system 100 may produce oneconversion timing model which addresses multiple funnel statesassociated with a funnel. In this example, multiple funnel states may beassociated with a single funnel, as illustrated in FIG. 5a and FIG. 5b .Each funnel state may be modeled with a weighted mixture of gammadistributions, with each gamma distribution having a scale and shape. Inan example, the total number of independent gamma functions used tomodel the funnel states may be reduced to a target number of modes. Asthe number of independent gamma functions used to model the funnelstates shrinks, the goodness of fit to the distribution of conversiontimespans for the population of converters is expected to suffer;however, this technique can reduce the chances of over-fitting the data,and may improve the predictive quality of the resulting models. In somecases, the final number of gamma distributions used to model the funnelstates for the conversion timing model, is pre-configured andcorresponds to the number of different conversion modes. For theconversion timing model, each funnel state is fitted with a weightedcombination of gamma distributions (i.e. gamma mixture model) selectedfrom the same set of gamma distributions each modeling a conversionmode. The overall conversion timing model which addresses multiplefunnel states can simply be a set of gamma mixture models sharing thesame gamma distribution components but with different weight vectorswith each weight vector corresponding to one funnel state model. Tofurther reduce the model parameter size, we can use simple forms ofparameterized functions (such as a linear function) to map from theobserved funnel events directly to the weight vectors, instead ofexplicitly defining disjoint funnel states based on funnel events as inFIG. 5a and FIG. 5b . It is straightforward to find the optimalparameters (scale and shape for the gamma distributions, and weightvector corresponding to funnel states) for the model using standardoptimization algorithms such as expectation-maximization (EM) algorithm.The number of gamma distribution components (number of modes) can alsobe systematically determined using standard model selectionmethodologies.

The order of the steps in the foregoing described methods of theinvention are not intended to limit the invention; the steps may berearranged.

Foregoing described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. In particular, it is contemplatedthat functional implementation of invention described herein may beimplemented equivalently in hardware, software, firmware, and/or otheravailable functional components or building blocks, and that networksmay be wired, wireless, or a combination of wired and wireless. Othervariations and embodiments are possible in light of above teachings, andit is thus intended that the scope of invention not be limited by thisDetailed Description, but rather by Claims following.

What is claimed is:
 1. A computer-implemented method comprising:receiving, from a data repository, histories of a plurality of convertedentities, each history comprising time-stamped records of eventsassociated with a respective entity; calculating a conversion timespanfor each converted entity by: selecting a time-stamped conversion eventfrom a respective converted entity's history according to conversionevent criteria; selecting a time-stamped entry event occurring beforethe time-stamped conversion event from the respective converted entity'shistory according to entry event criteria; and calculating an elapsedtime between the time-stamped entry event and the time-stampedconversion event; constructing a conversion timing model based on adistribution of the calculated conversion timespans, wherein theconversion timing model is configured to predict a likelihood ofconversion for an entity based on the entity's elapsed time since theentity's time-stamped entry event meeting the entry event criteria;receiving, from a real time bidding exchange, notification of anopportunity to bid on an advertising exposure associated with acandidate entity; responsive to receiving notification of theopportunity to bid, determining a time stamp of a qualified entry eventof the candidate entity, according to the entry event criteria;identifying the candidate entity's elapsed time since the candidateentity's qualified entry event; calculating a likelihood of conversionfor the candidate entity by applying the conversion timing model to theelapsed time since the candidate entity's qualified entry event; andpreparing a response to the notification based on the likelihood ofconversion for the candidate entity.
 2. The method of claim 1 wherein:the notification comprises a bid request.
 3. The method of claim 1wherein: calculating comprises predicting a likelihood of conversionwithin a time window; and preparing comprises preparing a response tothe notification based on the likelihood of conversion for the candidateentity within the time window.
 4. The method of claim 1 wherein:constructing comprises fitting the distribution of calculated conversiontimespans with a gamma mixture model configured with a number ofconversion modes.
 5. The method of claim 1 wherein: preparing a responsecomprises selecting a bid price.
 6. The method of claim 1 wherein:preparing a response comprises selecting an advertising creative.
 7. Themethod of claim 1 wherein: preparing comprises selecting customizedcontent.
 8. A system comprising: a processor; a computer readablestorage medium storing processor-executable computer programinstructions, the instructions comprising instructions for: receiving,from a data repository, histories of a plurality of converted entities,each history comprising time-stamped records of events associated with arespective entity; calculating a conversion timespan for each convertedentity by: selecting a time-stamped conversion event from a respectiveconverted entity's history according to conversion event criteria;selecting a time-stamped entry event occurring before the time-stampedconversion event from the respective converted entity's historyaccording to entry event criteria; and calculating an elapsed timebetween the time-stamped entry event and the time-stamped conversionevent; constructing a conversion timing model based on a distribution ofthe calculated conversion timespans, wherein the conversion timing modelis configured to predict a likelihood of conversion for an entity basedon the entity's elapsed time since the entity's time-stamped entry eventmeeting the entry event criteria; receiving, from a real time biddingexchange, notification of an opportunity to bid on an advertisingexposure associated with a candidate entity; responsive to receivingnotification of the opportunity to bid, determining a time stamp of aqualified entry event of the candidate entity, according to the entryevent criteria; identifying the candidate entity's elapsed time sincecandidate entity's qualified entry event; calculating a likelihood ofconversion for the candidate entity by applying the conversion timingmodel to the elapsed time since the candidate entity's qualified entryevent; and preparing a response to the notification based on thelikelihood of conversion for the candidate entity.
 9. The system ofclaim 8 wherein: the notification comprises a bid request.
 10. Thesystem of claim 8 wherein: calculating comprises predicting a likelihoodof conversion within a time window; and preparing comprises preparing aresponse to the notification based on the likelihood of conversion forthe candidate entity within the time window.
 11. The system of claim 8wherein: constructing comprises fitting the distribution of calculatedconversion timespans with a gamma mixture model configured with a numberof conversion modes.
 12. A non-transitory computer readable storagemedium executing computer program instructions, the computer programinstructions comprising instructions for: receiving, from a datarepository, histories of a plurality of converted entities, each historycomprising time-stamped records of events associated with a respectiveentity; calculating a conversion timespan for each converted entity by:selecting a time-stamped conversion event from a respective convertedentity's history according to conversion event criteria; selecting atime-stamped entry event occurring before the time-stamped conversionevent from the respective converted entity's history according to entryevent criteria; and calculating an elapsed time between the time-stampedentry event and the time-stamped conversion event; constructing aconversion timing model based on a distribution of the calculated,conversion timespans, wherein the conversion timing model is configuredto predict a likelihood of conversion for an entity based on theentity's elapsed time since the entity's time-stamped entry eventmeeting the entry event criteria; receiving, from a real time biddingexchange, notification of an opportunity to bid on an advertisingexposure associated with a candidate entity; responsive to receivingnotification of the opportunity to bid, determining a time stamp of aqualified entry event of the candidate entity, according to the entryevent criteria; identifying the candidate entity's elapsed time sincethe candidate entity's qualified entry event; calculating a likelihoodof conversion for the candidate entity by applying the conversion timingmodel to the elapsed time since the candidate entity's qualified entryevent; and preparing a response to the notification based on thelikelihood of conversion for the candidate entity.
 13. The medium ofclaim 12 wherein: calculating comprises predicting a likelihood ofconversion within a time window; and preparing comprises preparing aresponse to the notification based on the likelihood of conversion forthe candidate entity within the time window.
 14. The medium of claim 12wherein: constructing comprises fitting the distribution of calculatedconversion timespans with a gamma mixture model configured with a numberof conversion modes.
 15. The medium of claim 12 wherein: thenotification comprises a bid request.
 16. The method of claim 5 furthercomprising: bidding on the opportunity at the selected bid price. 17.The method of claim 6 further comprising: sending the selectedadvertising creative to the candidate entity.
 18. The method of claim 7further comprising: sending the selected customized content to thecandidate entity.
 19. The system of claim 8 wherein: preparing aresponse comprises selecting a bid price; and the instructions furthercomprise instructions for: bidding on the opportunity at the selectedbid price.
 20. The medium of claim 12 wherein: preparing a responsecomprises selecting a bid price; and the computer program instructionsfurther comprise instructions for: bidding on the opportunity at theselected bid price.